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Testing Games:Randomizing Regression Tests Using

Game Theory

Nupul Kukreja, William G.J. Halfond, Milind Tambe & Manish Jain

Annual Research ReviewApril 30, 2014

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Outline• Motivation• Problem(s) with traditional test scheduling• Game Theory and Randomization• Modeling software testing as a 2-player game• Evaluation• Conclusion & Future Work

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Motivation

Software Size

Regression Suite

DEVS

The deadline is close too!

Dude! Suite XX is not

gonna run!

Let’s CODE NOW FIX LATER

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Motivating Problem(s)• Existing test case scheduling activities are

deterministic• Developers know which test cases will be

executed when• Developers can check in insufficiently tested

code closer to delivery deadline• High-turn around time for fixing bugs in low

priority features• Random test-scheduling helpful but treats

each test case as equally important

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Software Testing as a 2-Player Game

• This tension between software testers and developers can be modeled as a two-player game

• We solve the game to answer the following question:– Given an adaptive adversary (developers) and

resource constraints (testers) what is the optimum test-scheduling strategy that maximizes the tester’s expected payoff?

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Game Theory• Study of strategic decision making among

multiple players – corporations, software agents, testers and developers, regular humans etc.,

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Two-player “Security” Game

Adversary

Terminal 1 Terminal 2

Defender

Terminal 1-3 1

5 -1

Terminal 25 -1

-5 2

Security game assumptions:1. What is good for one player (+ve payoff) is bad for the other (-ve payoff)2. Adversary can conduct perfect surveillance and act appropriately i.e.,

these are simultaneous move games or Stackelberg games

60%

40%

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Testing GameDeveloper

Requirement 1

Check in ITC* Check in PC*

Tester Requirement 1

Test-3 1

5 -1

Don’t Test5 -1

-5 2

*ITC: Insufficiently tested code*PC: Perfect code i.e., 100% tested

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Testing Game – Payoffs• Payoffs are either positive or negative• Proportional to the value of the requirement

for both, the tester & developer• Payoffs can be derived in many ways:– Directly from requirement priorities– Expert judgment and/or planning poker– Delphi methods– Directly from test-case priorities

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Defining Test Requirements• Could be black-box or white-box based• If black-box, TR may correspond to:– Module/component– Method– OR…the requirement as a whole

• “We” group test cases by requirements– Each requirement is ‘covered’ by one or more test

cases (or suites)

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Not All Developers Are The Same• Commonly encountered personality traits– Lazy/sloppy– New Grad– Moderate/Average– Seasoned Developer

• Each persona has a probability of “screwing up” i.e., checking in insufficiently tested code

• We can compute these probabilities by looking at the team composition

The Testing Game

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P(seasoned) = 2/10

P(sloppy) = 3/10

P(avg) = 5/10

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Solving the Testing Game

Probability of schedulingtest case ‘i'

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0.1398 0.1344 0.2307 0.4538 0.0414

Req 1 Req 2 Req 3 Req 4 Req 5

Tester 2 -10 7 -4 6 -1 9 -9 9 -9

Developer -7 4 -1 3 -6 5 -3 7 -10 3

Example Testing Game

Create a test case scheduling of ‘m’ test cases by sampling from the above distribution

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Evaluation• Large simulation:– 1000 test requirements = 1 Game

• 1000 Games randomly generated– Each game played/solved 1000 times over

– Payoffs range from [-10,10]– Constraint: Can only schedule/execute 500 test cases

• Compared with:– Deterministic test scheduling – Uniform Random test scheduling– Weighted Random test scheduling

• Tester-only weights• Tester+developer based weights

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Results

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Limitations and Threats to Validity• Developers not adversarial• Developers may choose to be sloppy at times

with a particular probability• Lack of perfect historical observation for

developers• Expected payoffs is mostly a mathematical

notation

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Conclusion & Future Work• New approach for test case scheduling using Game

Theory– Accounts for tester and developer’s payoffs

• Randomizing test cases acts as deterrent for developers, for checking in insufficiently tested code

• The test case distribution is optimum under resource constraints and maximizes payoff for worst case developer behavior – robust!

• Simulation shows positive results and is a first step to analyzing the tester/developer relationship

DEVS

Adversary

Terminal 1 Terminal 2

Defender

Terminal 1-3 1

5 -1

Terminal 25 -1

-5 2

Thank you!Questions?

0.1398 0.1344 0.2307 0.4538 0.0414

Req 1 Req 2 Req 3 Req 4 Req 5

Tester 2 -10 7 -4 6 -1 9 -9 9 -9

Developer -7 4 -1 3 -6 5 -3 7 -10 3